Doppler ultrasound investigation of uterine and ovarian blood flow in infertility and early pregnancy

This review describes the current use of Doppler ultrasound to examine blood flow in the uterus and ovaries in infertile patients and during early pregnancy. The basics of Doppler ultrasound and the different methods of measuring blood flow are discussed from the viewpoint of the clinician who may be unfamiliar with Doppler physics and terminology. Normal values in the menstrual cycle and the relationship of uterine and ovarian blood flow to infertility and to implantation following in-vitro fertilization are presented. Normal values for uterine blood flow in the first 16 weeks of pregnancy and the effect of sex steroids and ovulation induction on their values are described. The possible relationship of defective uterine blood flow to recurrent abortion is examined. New areas of investigation, such as the effect of standing on blood flow, and the effect of drugs are explored. The findings of this review indicate that Doppler blood flow studies may provide significant information about possible causes of some disorders of infertility and early pregnancy and methods of treatment for the same.     

How to share a secret with cheaters

This paper demonstrates that Shamir's scheme [10] is not secure against certain forms of cheating. A small modification to his scheme retains the security and efficiency of the original, is secure against these forms of cheating, and preserves the property that its security does not depend on any unproven assumptions such as the intractability of computing number-theoretic functions.This material is based in part upon work supported by the National Science Foundation under Grant Nos. DCR-8301212 and DCR-8352093. Part of the work was performed while the second author was a visitor at the IBM Thomas J. Watson Research Center.     

Very first impressions.

First impressions of people's personalities are often formed by using the visual appearance of their faces. Defining how quickly these impressions can be formed has critical implications for understanding social interactions and for determining the visual properties used to shape them. To study impression formation independent of emotional cues, threat judgments were made on faces with a neutral expression. Consequently, participants' judgments pertained to the personality rather than to a certain temporary emotional state (e.g., anger). The results demonstrate that consistent first impressions can be formed very quickly, based on whatever information is available within the first 39 ms. First impressions were less consistent under these conditions when the judgments were about intelligence, suggesting that survival-related traits are judged more quickly. The authors propose that low spatial frequencies mediate this swift formation of threat judgments and provide evidence that supports this hypothesis. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Metals and incinerators: The latest regulatory phase

Recent regulations in the United States controlling emissions of metals and halogen acid gases from boilers and industrial furnaces (BIFs) burning regu     

Great Lakes rivermouth ecosystems: Scientific synthesis and management implications

At the interface of the Great Lakes and their tributary rivers lies the rivermouths, a class of aquatic ecosystem where lake and lotic processes mix and distinct features emerge. Many rivermouths are the focal point of both human interaction with the Great Lakes and human impacts to the lakes; many cities, ports, and beaches are located in rivermouth ecosystems, and these human pressures often degrade key ecological functions that rivermouths provide. Despite their ecological uniqueness and apparent economic importance, there has been relatively little research on these ecosystems as a class relative to studies on upstream rivers or the open-lake waters. Here we present a synthesis of current knowledge about ecosystem structure and function in Great Lakes rivermouths based on studies in both Laurentian rivermouths, coastal wetlands, and marine estuarine systems. A conceptual model is presented that establishes a common semantic framework for discussing the characteristic spatial features of rivermouths. This model then is used to conceptually link ecosystem structure and function to ecological services provided by rivermouths. This synthesis helps identify the critical gaps in understanding rivermouth ecology. Specifically, additional information is needed on how rivermouths collectively influence the Great Lakes ecosystem, how human alterations influence rivermouth functions, and how ecosystem services provided by rivermouths can be managed to benefit the surrounding socioeconomic networks.     

Stabilization of Glucagon by Trehalose Glycopolymer Nanogels

Glucagon is a peptide hormone used for the treatment of hypoglycemia; however, its clinical potential is limited by its insolubility and instability in solution. Herein, the encapsulation, stabilization, and release of glucagon by trehalose glycopolymer nanogels are reported. Methacrylate‐functionalized trehalose is copolymerized with pyridyl disulfide ethyl methacrylate using free radical polymerization conditions to form trehalose glycopolymers with thiol‐reactive handles. Glucagon is chemically modified to contain two thiol groups and is subsequently utilized as the cross‐linker to form redox‐responsive trehalose nanogels with greater than 80% conjugation yield. Nanogel formation and subsequent glucagon stabilization are characterized using polyacrylamide gel electrophoresis, dynamic light scattering, and transmission electron microscopy. It is determined that the solution stability of the glucagon increased from less than 24 h to at least three weeks in the nanogel form. Additionally, in vitro activity of the synthesized glucagon analog and released glucagon is investigated, demonstrating that the glucagon remains active after modification. It is anticipated that these glucagon–nanogel conjugates will be useful as a stabilizing glucagon formulation, allowing for cargo release under mild reducing conditions.     

Elucidating the Effect of Planar Graphitic Layers and Cylindrical Pores on the Storage and Diffusion of Li,Na, and K in Carbon Materials

Hard carbons are among the most promising materials for alkali‐ion metal anodes. These materials have a highly complex structure and understanding the metal storage and migration within these structures is of utmost importance for the development of next‐generation battery technologies. The effect of different carbon structural motifs on Li, Na, and K storage and diffusion are probed using density functional theory based on experimental characterizations of hard carbon samples. Two carbon structural models—the planar graphitic layer model and the cylindrical pore model—are constructed guided by small‐angle X‐ray scattering and transmission electron microscopy characterization. The planar graphitic layers with interlayer distance <6.5 Å are beneficial for metal storage, but do not have significant contribution to rapid metal diffusion. Fast diffusion is shown to take place in planar graphitic layers with interlayer distance >6.5 Å, when the graphitic layer separation becomes so wide that there is negligible interaction between the two graphitic layers. The cylindrical pore model, reflecting the curved morphology, does not increase metal storage, but significantly lowers the metal migration barriers. Hence, the curved carbon morphologies are shown to have great importance for battery cycling. These findings provide an atomic‐scale picture of the metal storage and diffusion in these materials.     

Enhanced decision tree ensembles for land-cover mapping from fully polarimetric SAR data

Fully polarimetric synthetic aperture radar (PolSAR) Earth Observations showed great potential for mapping and monitoring agro-environmental systems. Numerous polarimetric features can be extracted from these complex observations which may lead to improve accuracy of land-cover classification and object characterization. This article employed two well-known decision tree ensembles, i.e. bagged tree (BT) and random forest (RF), for land-cover mapping from PolSAR imagery. Moreover, two fast modified decision tree ensembles were proposed in this article, namely balanced filter-based forest (BFF) and cost-sensitive filter-based forest (CFF). These algorithms, designed based on the idea of RF, use a fast filter feature selection algorithms and two extended majority voting. They are also able to embed some solutions of imbalanced data problem into their structures. Three different PolSAR datasets, with imbalanced data, were used for evaluating efficiency of the proposed algorithms. The results indicated that all the tree ensembles have higher efficiency and reliability than the individual DT. Moreover, both proposed tree ensembles obtained higher mean overall accuracy (0.5–14% higher), producer’s accuracy (0.5–10% higher), and user’s accuracy (0.5–9% higher) than the classical tree ensembles, i.e. BT and RF. They were also much faster (e.g. 2–10 times) and more stable than their competitors for classification of these three datasets. In addition, unlike BT and RF, which obtained higher accuracy in large ensembles (i.e. the high number of DT), BFF and CFF can also be more efficient and reliable in smaller ensembles. Furthermore, the extended majority voting techniques could outperform the classical majority voting for decision fusion.     

Multiple kernel representation and classification of multivariate satellite-image time-series for crop mapping

Multivariate satellite-image time-series (MSITS) are a valuable source of information for a wide range of agricultural applications. Image classification, one of the main applications of this type of data, is a challenging task. It is mainly because MSITS are generated by a complex interaction among several sources of information, which are known as the factors of variation. These factors contain different information with different levels of relevance to a classification task. Thus, a proper representation of MSITS data is required in order to extract and model the most useful information from these factors for classification purpose. To this end, this article proposes three multiple kernel representations of MSITS data. These representations extract the most classification-related information from these data through combining the basis kernels constructed from different factors of variation of the MSITS data. In the proposed representations, the combination of the basis kernels was achieved by using the multiple kernel learning algorithms. The efficiency of the proposed multiple kernel representations was evaluated based both on analysing the relevance of their kernels to the classification task and their classification performances. Two different MSITS data sets composed of 10 RapidEye imageries of an agricultural area were used to evaluate the performances of the proposed methods. In addition, the classification results of both MSITS using a single kernel were considered as the baseline for comparison. The results showed an increase of up to 14% in overall accuracy of the classification maps by using the multiple kernel representations. Moreover, these particular representations for classification of time-series observations were able to handle the undesirable effects in image data such as the presence of clouds and their shadows.